Provided is a control method for a system for determining a lesion obtained via a real-time image. The control method comprises the steps of: an endoscope device obtaining a stomach endoscopy image; the endoscope device transmitting the obtained stomach endoscopy image to a server; the server determining a lesion included in the stomach endoscopy image, by inputting the stomach endoscopy image into a first artificial intelligence model; when it is determined that a lesion is detected in the stomach endoscopy image, the server obtaining an image including the lesion and transmitting the image to a database of the server; the server determining the type of the lesion included in the image, by inputting the image into a second artificial intelligence model; and when it is determined that a lesion is detected in the stomach endoscopy image, a display device displaying a UI for guiding the location of the lesion in the stomach endoscopy image.

Various methods and systems are described for obtaining at least one CTA perfusion functional map from Time Resolved Helical CTA (TRH-CTA) image data. At least one processor may be configured to preprocess the TRH-CTA helical image data to generate preprocessed TRH-CTA helical image data; generate time density curve data for a plurality of voxels from the preprocessed TRH-CTA helical image data for an axial imaging slice, where the time density curve data comprise intensity values for different phases of the preprocessed TRH-CTA helical image data arranged sequentially in time; generate at least one perfusion functional map for the axial imaging slice by at least one of: (1) applying at least one mapping function to different phases of the time density curve data corresponding to the axial imaging slice; (2) applying a deconvolution method to the time density curve data; and (3) applying a non-deconvolution method to the time density curve data; and perform spatial filtering on the perfusion functional map. A display may be used to display at least one filtered perfusion functional map.

A system and method are provided for creating an image including quantified flow within vessels of a subject. The method includes providing a single-sweep, three-dimensional (3D) image volume acquired from a subject during a single pass of a computed tomography (CT) imaging system as the subject receives a dose of a contrast agent and determining a phase shift corresponding to pulsatile contrast in vessels within the single-sweep, 3D image volume. The method further includes quantifying a flow through the vessels within the single-sweep, 3D image volume using the phase shift and generating a report including indicating flow through the vessels within the 3D image volume.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a clinical recommendation for medical treatment of a patient. In one aspect a method comprises: receiving multi-modal data characterizing a patient, wherein the multi-modal data comprises a respective feature representation for each of a plurality of modalities; processing the multi-modal data characterizing the patient using a machine learning model, in accordance with values of a set of machine learning model parameters, to generate a patient classification that classifies the patient as being included in a patient category from a set of patient categories; determining an uncertainty measure that characterizes an uncertainty of the patient classification generated by the machine learning model; and generating a clinical recommendation for medical treatment of the patient based on: (i) the patient classification, and (ii) the uncertainty measure that characterizes the uncertainty of the patient classification.

A vascular assessment apparatus is disclosed. The apparatus is configured to receive medical images of a coronary vessel tree of a subject from a medical imaging device and analyze the medical images to identify vessel segments within the coronary vessel tree. For each identified vessel segment, the apparatus is configured to determine flow rates at each identified vessel segment and calculate an index indicative of vascular function based on the determined flow rates.

The present invention relates to the field of medical monitoring, and in particular non-contact monitoring of one or more physiological parameters in a region of a patient during surgery. Systems, methods, and computer readable media are described for generating a pulsation field and/or a pulsation strength field of a region of interest (ROI) in a patient across a field of view of an image capture device, such as a video camera. The pulsation field and/or the pulsation strength field can be generated from changes in light intensities and/or colors of pixels in a video sequence captured by the image capture device. The pulsation field and/or the pulsation strength field can be combined with indocyanine green (ICG) information regarding ICG dye injected into the patient to identify sites where blood flow has decreased and/or ceased and that are at risk of hypoxia.

The present invention relates to the field of medical monitoring, and in particular non-contact monitoring of one or more physiological parameters in a region of a patient during surgery. Systems, methods, and computer readable media are described for generating a pulsation field and/or a pulsation strength field of a region of interest (ROI) in a patient across a field of view of an image capture device, such as a video camera. The pulsation field and/or the pulsation strength field can be generated from changes in light intensities and/or colors of pixels in a video sequence captured by the image capture device. The pulsation field and/or the pulsation strength field can be combined with indocyanine green (ICG) information regarding ICG dye injected into the patient to identify sites where blood flow has decreased and/or ceased and that are at risk of hypoxia.

Computer-implemented methods and systems are provided for quantitative hemodynamic flow analysis, which involves retrieving patient specific image data. A 3D reconstruction of a vessel of interest can be created from the patient specific image data. Geometric information can be extracted from the 3D reconstruction. A lesion position can be determined. Patient specific data can be obtained. Hemodynamic results can be calculated based on the geometric information, the lesion position and the patient specific data.

Techniques for processing one or more frames of an angiogram are disclosed. The processing may take place during or after an angiography exam. The one or more frames of the angiogram are acquired during the angiography exam. The one or more frames are processed to determine, based on at least one pre-defined criterion, whether the angiogram at least comprises one frame with a diagnostic value among the one or more frames. If the angiogram comprises at least one frame with the diagnostic value, based on the angiogram, a score quantifying the diagnostic value of the angiogram is determined using a trained machine-learning (ML) algorithm. Techniques for processing, e.g., ranking/sorting, multiple angiograms associated with an anatomical region of interest of a patient are also provided, by which a respective score for each of the multiple angiograms is determined using the techniques for processing one or more frames of an angiogram.

A method for visualizing luminance variance for an object may include receiving image data associated with a digital image of the object. An algorithm is applied to the image data to generate an enhanced image. The enhanced image includes connected pixel value lines representative of pixel value ranges from the input image to enable visualization of luminance variance of the object by the human eye.